library(groundhog)
## Loaded 'groundhog' (version:2.0.1) using R-4.2.1
## Tips and troubleshooting: https://groundhogR.com
## groundhog says:
## 
## 
## 
##           OUTDATED GROUNDHOG
##             You are using version  '2.0.1
##             The current version is '2.1.0'
## 
##             You can read about the changes here: https://groundhogr.com/changelog
## 
## Update by running: 
## install.packages('groundhog')
pkgs <-  c("lmerTest", "ggeffects","r2glmm", "tidyverse","here", "sjPlot", "ggpubr", "wesanderson", "effectsize","broom.mixed","corrr","report", "ez", "ggdist")
groundhog.day <- '2022-07-25'
groundhog.library(pkgs, groundhog.day)
## Loading required package: lme4
## Loading required package: Matrix
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here::i_am("Study 2/Analysis/SAs2_Analysis.Rmd")
## here() starts at /Users/jacobelder/Documents/GitHub/SelfAnchoring
#plotDir <- "/Volumes/Research Project/Trait_TestRetest/WeekTRT/plots/"
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
## ℹ SHA-1 hash of file is "07e3c11d2838efe15b1a6baf5ba2694da3f28cb1"
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")
## ℹ SHA-1 hash of file is "374a4de7fec345d21628a52c0ed0e4f2c389df8e"
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/named.effects.ref.R")
## ℹ SHA-1 hash of file is "0a5b928a75d310573e96ab72631b77a8a8b9acb3"
# fullTest <- read.csv("../Cleaning/output/fullTest.csv")
# fullTrain <- read.csv("../Cleaning/output/fullTrain.csv")
# traitsFreqs <- read.csv("../Cleaning/output/traitFreqOverUnder.csv")

fullTest <- arrow::read_parquet("../Cleaning/output/fullTest.parquet")
fullTrain <- arrow::read_parquet("../Cleaning/output/fullTrain.parquet")
traitsFreqs <- arrow::read_parquet("../Cleaning/output/traitFreqOverUnder.parquet")

uSubs <- unique(fullTest$subID)

indDiffs <- fullTest[!duplicated(fullTest$subID),]

allPosCents <- read.csv("/Volumes/GoogleDrive/My Drive/Volumes/Research Project/Trait Network_Behaviral/generating network/output/allPosCents.csv")
fullTest$ingChoiceN <- as.factor(fullTest$ingChoiceN)
fullTest$novel <- as.factor(fullTest$novel)
fullTest$selfResp.Z <- scale(fullTest$selfResp)
fullTest$SE.Z <- scale(fullTest$SE)
fullTest$iSE.Z <- scale(fullTest$iSE)
fullTest$oSE.Z <- scale(fullTest$oSE)
fullTest$predicted.Z <- scale(fullTest$predicted)
fullTest$slope.Z <- scale(fullTest$slope)
fullTest$entropy.Z <- scale(fullTest$entropy)
fullTest$WSR.Z <- scale(fullTest$WSR)
fullTest$neighAveOutSE.Z <- scale(fullTest$neighAveOutSE)
fullTest$neighAveAllSE.Z <- scale(fullTest$neighAveAllSE)
fullTest$neighAveInSE.Z <- scale(fullTest$neighAveInSE)
fullTest$evalLOO.Z <- scale(fullTest$evalLOO)
fullTest$propCorrInLOO.Z <- scale(fullTest$propCorrInLOO)
fullTest$propCorrOutLOO.Z <- scale(fullTest$propCorrOutLOO)
#fullTest$propCorr.Z <- scale(fullTest$propCorr)
fullTest$desirability.Z <- scale(fullTest$desirability)
fullTest$er.Z <- scale(fullTest$er)
fullTest$inDegree.Z <- scale(fullTest$inDegree)
fullTest$outDegree.Z <- scale(fullTest$outDegree)
fullTest$Ent.Z <- scale(fullTest$Ent)

fullTest$outgroup <- as.factor(fullTest$outgroup)
fullTest$outgroup <- relevel(fullTest$outgroup,"Not UCR")
fullTest$OtherTherm <- rowMeans(fullTest[c("Therm_2","Therm_4")])
fullTest$InOtherTherm <- fullTest$Therm_1 - fullTest$OtherTherm
fullTest$OtherStatus <- rowMeans(fullTest[c("UCLA_Status","CSULA_Status")])
fullTest$InOtherStatus <- fullTest$UCR_Status - fullTest$OtherStatus

fullTest$InOutStatus <- ifelse(fullTest$outgroup == "UCLA", fullTest$InUCLAStatus, 
       ifelse(fullTest$outgroup == "CSU LA", fullTest$InCSULAStatus,
              ifelse(fullTest$outgroup == "Not UCR", fullTest$InOtherStatus, NA)))
fullTest$InOutTherm <- ifelse(fullTest$outgroup == "UCLA", fullTest$InUCLATherm, 
       ifelse(fullTest$outgroup == "CSU LA", fullTest$InCSULATherm,
              ifelse(fullTest$outgroup == "Not UCR", fullTest$InOtherTherm, NA)))
fullTest$novel <- as.factor(fullTest$novel)
levels(fullTest$novel) <- list("Trained"  = "0", "Held Out" = "1")

fullTest$outgroup <- as.factor(fullTest$outgroup)
fullTest$outgroup <- relevel(fullTest$outgroup,"Not UCR")
PCA<- prcomp(na.omit(fullTest[c("predicted","neighAveOutSE")]),
                center = TRUE,
                scale. = TRUE)
fullTest$PCA[!is.na(fullTest$predicted) & !is.na(fullTest$neighAveOutSE)] <- PCA$x[,1]
propMatrix <- matrix(nrow=148,ncol=7)
for(i in 1:148){
    traitDf <- subset(fullTest, Idx==i)
    test <- t.test(as.numeric(traitDf$ingChoiceN)-1, mu=.50)
    propMatrix[i, ] <- c(i, test$statistic, test$p.value, test$conf.int, test$estimate, test$parameter)
}
colnames(propMatrix) <- c("Idx", "stat", "p", "LCI", "UCI", "est", "param")
propMatrix <- as.data.frame(propMatrix)
propMatrix$trait <- traitsFreqs$trait[1:148]
propMatrix <- propMatrix[order(propMatrix$p),]

propMatrix

Are there differences in the perceptions of status between universities?

UCR students perceive UCLA as being significantly higher status and CSU LA students as being significantly lower status.

indDiffs$Status <- NULL
statusDf <- pivot_longer(indDiffs, cols=ends_with("_Status"), names_to="University", values_to="Status") %>% select(subID, University, Status) %>% drop_na()
statusDf$University <- gsub("_Status","", statusDf$University)

statusDf$University <- as.factor(statusDf$University)
statusDf$subID <- as.factor(statusDf$subID)
statusDf$University <- relevel(statusDf$University, ref = "UCR")

m <- lmer( scale(Status) ~ University +  ( 1  | subID), data = statusDf)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Status) ~ University + (1 | subID)
##    Data: statusDf
## 
## REML criterion at convergence: 1169.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.1951 -0.5157  0.0584  0.5731  2.8926 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.08838  0.2973  
##  Residual             0.33374  0.5777  
## Number of obs: 599, groups:  subID, 200
## 
## Fixed effects:
##                  Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)      -0.14003    0.04594 548.12194  -3.048  0.00242 ** 
## UniversityCSULA  -0.71545    0.05786 397.80645 -12.366  < 2e-16 ***
## UniversityUCLA    1.13170    0.05777 397.29760  19.590  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) UCSULA
## UnvrstCSULA -0.628       
## UnvrstyUCLA -0.629  0.499
ggpredict(m, c("University")) %>% plot()

m<-ezANOVA(statusDf, dv = Status, wid = subID, between = as.factor(University) )
## Warning: The column supplied as the wid variable contains non-unique values
## across levels of the supplied between-Ss variables. Automatically fixing this by
## generating unique wid labels.
## Warning: Data is unbalanced (unequal N per group). Make sure you specified a
## well-considered value for the type argument to ezANOVA().
## Coefficient covariances computed by hccm()
m
## $ANOVA
##       Effect DFn DFd        F             p p<.05       ges
## 1 University   2 596 410.2656 9.077901e-113     * 0.5792539
## 
## $`Levene's Test for Homogeneity of Variance`
##   DFn DFd      SSn      SSd        F          p p<.05
## 1   2 596 5.413247 569.5049 2.832544 0.05965564
ggplot(statusDf, aes(University, Status)) + 
  ggdist::stat_halfeye(adjust = .5, width = .7, .width = 0, justification = -.2, point_colour = NA) + 
  geom_boxplot(width = .2, outlier.shape = NA) + 
  geom_jitter(width = .05, alpha = .3) + labs(y="Perceived Status",x="University") + jtools::theme_apa()

Do people feel differ in how warmly they feel towards each university?

UCR students feel the most warmth towards UCR, significantly less towards UCLA, and the least towards CSU LA.

warmthDf <- pivot_longer(indDiffs, cols=starts_with("Therm_"), names_to="University", values_to="Warmth") %>% select(subID, University, Warmth) %>% drop_na()
warmthDf$University <- gsub("Therm_1","UCR", warmthDf$University)
warmthDf$University <- gsub("Therm_2","UCLA", warmthDf$University)
warmthDf$University <- gsub("Therm_4","CSU LA", warmthDf$University)

warmthDf$University <- as.factor(warmthDf$University)
warmthDf$subID <- as.factor(warmthDf$subID)
warmthDf$University <- relevel(warmthDf$University, ref = "UCR")

m <- lmer( scale(Warmth) ~ University +  ( 1  | subID), data = warmthDf)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Warmth) ~ University + (1 | subID)
##    Data: warmthDf
## 
## REML criterion at convergence: 1331.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1404 -0.5117  0.1239  0.6562  2.3007 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.08294  0.288   
##  Residual             0.64005  0.800   
## Number of obs: 529, groups:  subID, 197
## 
## Fixed effects:
##                   Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)        0.51774    0.06120 514.28694   8.460 2.78e-16 ***
## UniversityCSU LA  -1.30600    0.08721 364.74221 -14.976  < 2e-16 ***
## UniversityUCLA    -0.41053    0.08272 344.19662  -4.963 1.09e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) UCSULA
## UnvrstCSULA -0.623       
## UnvrstyUCLA -0.656  0.463
ggpredict(m, c("University")) %>% plot()

m<-ezANOVA(warmthDf, dv = Warmth, wid = subID, between = as.factor(University) )
## Warning: The column supplied as the wid variable contains non-unique values
## across levels of the supplied between-Ss variables. Automatically fixing this by
## generating unique wid labels.
## Warning: Data is unbalanced (unequal N per group). Make sure you specified a
## well-considered value for the type argument to ezANOVA().
## Coefficient covariances computed by hccm()
m
## $ANOVA
##       Effect DFn DFd        F            p p<.05      ges
## 1 University   2 526 102.2002 3.182127e-38     * 0.279847
## 
## $`Levene's Test for Homogeneity of Variance`
##   DFn DFd      SSn      SSd        F            p p<.05
## 1   2 526 3624.271 90108.74 10.57815 3.132948e-05     *
indDiffs$groupHomoph.Z <- scale(indDiffs$groupHomoph)
ggplot(warmthDf, aes(University, Warmth)) + 
  ggdist::stat_halfeye(adjust = .5, width = .7, .width = 0, justification = -.2, point_colour = NA) + 
  geom_boxplot(width = .2, outlier.shape = NA) + 
  geom_jitter(width = .05, alpha = .3) + labs(y="Perceived Warmth",x="University") + jtools::theme_apa()

Correlates with Assortativity in Group Predictioons

Dialectical self and need to belong positively associated with group assortativity. Social identification, self-esteem, identity-centrality, public collectivr eself-est

indDiffs %>% select(groupHomoph, seHomoph, SStatus:IdImp) %>% corToOne(., "groupHomoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
indDiffs %>% select(groupHomoph, seHomoph, SStatus:IdImp) %>% plotCorToOne(., "groupHomoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

Differences in assortativity of group predictions

library(ez)
m<-ezANOVA(indDiffs[!is.na(indDiffs$groupHomoph),], dv=groupHomoph, wid=subID, between=outgroup)
## Warning: Converting "subID" to factor for ANOVA.
## Warning: Converting "outgroup" to factor for ANOVA.
## Warning: Data is unbalanced (unequal N per group). Make sure you specified a
## well-considered value for the type argument to ezANOVA().
## Coefficient covariances computed by hccm()
hsd <- TukeyHSD(aov(groupHomoph ~ outgroup, data=indDiffs[!is.na(indDiffs$groupHomoph),]))
print(m)
## $ANOVA
##     Effect DFn DFd        F            p p<.05       ges
## 1 outgroup   2 194 18.22331 5.590415e-08     * 0.1581565
## 
## $`Levene's Test for Homogeneity of Variance`
##   DFn DFd        SSn       SSd        F          p p<.05
## 1   2 194 0.01989084 0.4295431 4.491777 0.01238983     *
print(hsd)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = groupHomoph ~ outgroup, data = indDiffs[!is.na(indDiffs$groupHomoph), ])
## 
## $outgroup
##                        diff         lwr        upr     p adj
## Not UCR-CSU LA -0.007553224 -0.03543940 0.02033295 0.7983771
## UCLA-CSU LA     0.057477795  0.02969826 0.08525733 0.0000064
## UCLA-Not UCR    0.065031019  0.03714485 0.09291719 0.0000003
ezPlot(indDiffs[!is.na(indDiffs$groupHomoph),], groupHomoph, wid=subID, between=outgroup, x=.(outgroup))
## Warning: Converting "subID" to factor for ANOVA.
## Warning: Converting "outgroup" to factor for ANOVA.
## Warning: Data is unbalanced (unequal N per group). Make sure you specified a
## well-considered value for the type argument to ezANOVA().
## Coefficient covariances computed by hccm()
## Warning in ezStats(data = data, dv = dv, wid = wid, within = within, within_full
## = within_full, : Unbalanced groups. Mean N will be used in computation of FLSD

indDiffs$groupHomoph.Z <- scale(indDiffs$groupHomoph)
m <- lm(groupHomoph.Z ~ outgroup, data=indDiffs)
summary(m)
## 
## Call:
## lm(formula = groupHomoph.Z ~ outgroup, data = indDiffs)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8578 -0.6006 -0.1222  0.4185  2.9193 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.2288     0.1135  -2.016   0.0452 *  
## outgroupNot UCR  -0.1031     0.1612  -0.640   0.5231    
## outgroupUCLA      0.7845     0.1605   4.887 2.14e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9222 on 194 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.1582, Adjusted R-squared:  0.1495 
## F-statistic: 18.22 on 2 and 194 DF,  p-value: 5.59e-08
ggpredict(m, c("outgroup")) %>% plot(show.title=F, add.data=T)

ggplot(indDiffs, aes(outgroup, groupHomoph.Z)) + 
  ggdist::stat_halfeye(adjust = .5, width = .7, .width = 0, justification = -.2, point_colour = NA) + 
  geom_boxplot(width = .2, outlier.shape = NA) + 
  geom_jitter(width = .05, alpha = .3)
## Warning: Removed 3 rows containing missing values (stat_slabinterval).
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
## Warning: Removed 3 rows containing missing values (geom_point).

(Attempt to) replicate prior work: Ingroup favoritism exhibited by ascribing more positive traits to ingroup

Desirability predicts ingroup predictions.

m <- glmer( ingChoiceN ~ desirability.Z +  ( desirability.Z  | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: ingChoiceN ~ desirability.Z + (desirability.Z | subID) + (1 |  
##     trait)
##    Data: fullTest
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
##      AIC      BIC   logLik deviance df.resid 
##  35470.1  35519.8 -17729.1  35458.1    29325 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.3124 -0.9143  0.4457  0.7630  2.8230 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.76943  0.8772       
##         desirability.Z 0.05154  0.2270   0.58
##  trait  (Intercept)    0.14360  0.3789       
## Number of obs: 29331, groups:  subID, 200; trait, 148
## 
## Fixed effects:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     0.54560    0.07082   7.704 1.32e-14 ***
## desirability.Z  0.26431    0.03751   7.047 1.83e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## desirblty.Z 0.225
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.726
(1.502 – 1.983)
0.122 7.704 <0.001
desirability Z 1.303
(1.210 – 1.402)
0.049 7.047 <0.001
Random Effects
σ2 3.29
τ00 subID 0.77
τ00 trait 0.14
τ11 subID.desirability.Z 0.05
ρ01 subID 0.58
ICC 0.23
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.016 / 0.239

Desirability predicts ingroup predictions most strongly for not CSU LA, less so for UCLA, and least for CSU LA.

m <- glmer( ingChoiceN ~ desirability.Z * outgroup +  ( desirability.Z  | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: ingChoiceN ~ desirability.Z * outgroup + (desirability.Z | subID) +  
##     (outgroup | trait)
##    Data: fullTest
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
##      AIC      BIC   logLik deviance df.resid 
##  34327.2  34451.5 -17148.6  34297.2    29316 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3269 -0.8549  0.4170  0.7136  4.5129 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr       
##  subID  (Intercept)    0.62040  0.7877              
##         desirability.Z 0.05315  0.2305   0.65       
##  trait  (Intercept)    0.32728  0.5721              
##         outgroupCSU LA 0.43552  0.6599   -0.89      
##         outgroupUCLA   0.64980  0.8061   -0.12 -0.18
## Number of obs: 29331, groups:  subID, 200; trait, 148
## 
## Fixed effects:
##                               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                    0.97510    0.11056   8.820  < 2e-16 ***
## desirability.Z                 0.37058    0.06023   6.153 7.59e-10 ***
## outgroupCSU LA                -0.23383    0.15084  -1.550  0.12109    
## outgroupUCLA                  -1.04611    0.15608  -6.703 2.05e-11 ***
## desirability.Z:outgroupCSU LA -0.20015    0.07512  -2.664  0.00771 ** 
## desirability.Z:outgroupUCLA   -0.07658    0.08493  -0.902  0.36722    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dsrb.Z oCSULA otUCLA d.Z:CL
## desirblty.Z  0.286                            
## outgrpCSULA -0.736 -0.209                     
## outgropUCLA -0.602 -0.202  0.397              
## dsr.Z:CSULA -0.228 -0.813  0.326  0.162       
## dsrb.Z:UCLA -0.203 -0.352  0.148  0.278  0.117
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.651
(2.135 – 3.293)
0.293 8.820 <0.001
desirability Z 1.449
(1.287 – 1.630)
0.087 6.153 <0.001
outgroup [CSU LA] 0.791
(0.589 – 1.064)
0.119 -1.550 0.121
outgroup [UCLA] 0.351
(0.259 – 0.477)
0.055 -6.703 <0.001
desirability Z * outgroup
[CSU LA]
0.819
(0.707 – 0.948)
0.061 -2.664 0.008
desirability Z * outgroup
[UCLA]
0.926
(0.784 – 1.094)
0.079 -0.902 0.367
Random Effects
σ2 3.29
τ00 subID 0.62
τ00 trait 0.33
τ11 subID.desirability.Z 0.05
τ11 trait.outgroupCSU LA 0.44
τ11 trait.outgroupUCLA 0.65
ρ01 subID 0.65
ρ01 trait.outgroupCSU LA -0.89
ρ01 trait.outgroupUCLA -0.12
ICC 0.25
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.061 / 0.296
ggpredict(m, c("desirability.Z","outgroup")) %>% plot(show.title=F) + xlab("Desirability") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="desirability.Z [all]"` to get smooth plots.

Replication of prior self-anchoring findings: Self-evaluations predicting ingroup evaluations

No covariates

Self-descriptiveness predict ingroup predictions

m <- glmer( ingChoiceN ~ selfResp.Z  +  ( selfResp.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.690
(1.480 – 1.931)
0.115 7.727 <0.001
selfResp Z 1.325
(1.236 – 1.419)
0.047 7.977 <0.001
Random Effects
σ2 3.29
τ00 subID 0.67
τ00 trait 0.14
τ11 subID.selfResp.Z 0.15
ρ01 subID 0.23
ICC 0.22
N subID 200
N trait 148
Observations 17399
Marginal R2 / Conditional R2 0.018 / 0.238

With covariates

m <- glmer( ingChoiceN ~ selfResp.Z  + desirability.Z +  ( selfResp.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.693
(1.488 – 1.925)
0.111 8.001 <0.001
selfResp Z 1.316
(1.229 – 1.410)
0.046 7.830 <0.001
desirability Z 1.233
(1.161 – 1.308)
0.038 6.868 <0.001
Random Effects
σ2 3.29
τ00 subID 0.67
τ00 trait 0.09
τ11 subID.selfResp.Z 0.15
ρ01 subID 0.23
ICC 0.22
N subID 200
N trait 148
Observations 17399
Marginal R2 / Conditional R2 0.031 / 0.241

With desirability covariates and differences with outgroup

Self-evaluations are more predictive of ingroup choices for negation and least predictive for CSU LA.

m <- glmer( ingChoiceN ~ selfResp.Z * outgroup + desirability.Z +  ( selfResp.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: ingChoiceN ~ selfResp.Z * outgroup + desirability.Z + (selfResp.Z |  
##     subID) + (outgroup | trait)
##    Data: fullTest
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
##      AIC      BIC   logLik deviance df.resid 
##  20515.1  20639.3 -10241.6  20483.1    17383 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7795 -0.8366  0.4165  0.7034  4.4633 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr       
##  subID  (Intercept)    0.5237   0.7237              
##         selfResp.Z     0.1320   0.3633   0.22       
##  trait  (Intercept)    0.2724   0.5220              
##         outgroupCSU LA 0.4595   0.6778   -0.93      
##         outgroupUCLA   0.6077   0.7795   -0.12 -0.19
## Number of obs: 17399, groups:  subID, 200; trait, 148
## 
## Fixed effects:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                0.92908    0.10519   8.832  < 2e-16 ***
## selfResp.Z                 0.39008    0.05952   6.554 5.62e-11 ***
## outgroupCSU LA            -0.19067    0.14535  -1.312   0.1896    
## outgroupUCLA              -1.02031    0.14940  -6.829 8.53e-12 ***
## desirability.Z             0.17394    0.02367   7.348 2.02e-13 ***
## selfResp.Z:outgroupCSU LA -0.15741    0.08208  -1.918   0.0551 .  
## selfResp.Z:outgroupUCLA   -0.18701    0.08413  -2.223   0.0262 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) slfR.Z oCSULA otUCLA dsrb.Z sR.Z:L
## selfResp.Z   0.148                                   
## outgrpCSULA -0.748 -0.107                            
## outgropUCLA -0.607 -0.104  0.393                     
## desirblty.Z  0.004 -0.018  0.010 -0.003              
## slR.Z:CSULA -0.107 -0.720  0.150  0.075 -0.040       
## slfR.Z:UCLA -0.105 -0.702  0.076  0.151 -0.004  0.502
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.532
(2.060 – 3.112)
0.266 8.832 <0.001
selfResp Z 1.477
(1.314 – 1.660)
0.088 6.554 <0.001
outgroup [CSU LA] 0.826
(0.622 – 1.099)
0.120 -1.312 0.190
outgroup [UCLA] 0.360
(0.269 – 0.483)
0.054 -6.829 <0.001
desirability Z 1.190
(1.136 – 1.246)
0.028 7.348 <0.001
selfResp Z * outgroup
[CSU LA]
0.854
(0.727 – 1.003)
0.070 -1.918 0.055
selfResp Z * outgroup
[UCLA]
0.829
(0.703 – 0.978)
0.070 -2.223 0.026
Random Effects
σ2 3.29
τ00 subID 0.52
τ00 trait 0.27
τ11 subID.selfResp.Z 0.13
τ11 trait.outgroupCSU LA 0.46
τ11 trait.outgroupUCLA 0.61
ρ01 subID 0.22
ρ01 trait.outgroupCSU LA -0.93
ρ01 trait.outgroupUCLA -0.12
ICC 0.24
N subID 200
N trait 148
Observations 17399
Marginal R2 / Conditional R2 0.072 / 0.294
ggpredict(m, c("selfResp.Z", "outgroup")) %>% plot(show.title=F) + xlab("Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

Is it merely just average self-descriptiveness of the trait?

Even while controlling for the average of all other participants’ evaluations on each trait, participants’ evaluations are still predictive of ingroup predictions.

m <- glmer( ingChoiceN ~ selfResp.Z + evalLOO.Z + desirability.Z +  ( evalLOO.Z + selfResp.Z + desirability.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: ingChoiceN ~ selfResp.Z + evalLOO.Z + desirability.Z + (evalLOO.Z +  
##     selfResp.Z + desirability.Z | subID) + (1 | trait)
##    Data: fullTest
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
##      AIC      BIC   logLik deviance df.resid 
##  21030.4  21146.9 -10500.2  21000.4    17384 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.4521 -0.8920  0.4264  0.7479  3.1218 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr             
##  subID  (Intercept)    0.70911  0.8421                    
##         evalLOO.Z      0.02170  0.1473   -0.22            
##         selfResp.Z     0.13816  0.3717    0.24 -0.02      
##         desirability.Z 0.04506  0.2123    0.60  0.03  0.23
##  trait  (Intercept)    0.07885  0.2808                    
## Number of obs: 17399, groups:  subID, 200; trait, 148
## 
## Fixed effects:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     0.54011    0.06683   8.082 6.37e-16 ***
## selfResp.Z      0.25573    0.03466   7.378 1.61e-13 ***
## evalLOO.Z       0.15309    0.03428   4.465 7.99e-06 ***
## desirability.Z  0.17277    0.03518   4.911 9.08e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) slfR.Z eLOO.Z
## selfResp.Z   0.165              
## evalLOO.Z   -0.053 -0.114       
## desirblty.Z  0.246  0.071 -0.333
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.716
(1.506 – 1.956)
0.115 8.082 <0.001
selfResp Z 1.291
(1.207 – 1.382)
0.045 7.378 <0.001
evalLOO Z 1.165
(1.090 – 1.246)
0.040 4.465 <0.001
desirability Z 1.189
(1.109 – 1.273)
0.042 4.911 <0.001
Random Effects
σ2 3.29
τ00 subID 0.71
τ00 trait 0.08
τ11 subID.evalLOO.Z 0.02
τ11 subID.selfResp.Z 0.14
τ11 subID.desirability.Z 0.05
ρ01 subID.evalLOO.Z -0.22
ρ01 subID.selfResp.Z 0.24
ρ01 subID.desirability.Z 0.60
ICC 0.23
N subID 200
N trait 148
Observations 17399
Marginal R2 / Conditional R2 0.040 / 0.264

Does similarity-weighted self-evaluation average predict ingroup choices?

m <- glmer( ingChoiceN ~ WSR.Z + ( WSR.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.636
(1.342 – 1.994)
0.165 4.878 <0.001
WSR Z 1.871
(1.433 – 2.443)
0.255 4.607 <0.001
Random Effects
σ2 3.29
τ00 subID 1.29
τ00 trait 0.17
τ11 subID.WSR.Z 2.59
ρ01 subID 0.06
ICC 0.55
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.051 / 0.575

Covariates

m <- glmer( ingChoiceN ~ WSR.Z + desirability.Z + ( WSR.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.639
(1.351 – 1.990)
0.162 4.999 <0.001
WSR Z 1.846
(1.416 – 2.407)
0.250 4.529 <0.001
desirability Z 1.249
(1.174 – 1.329)
0.039 7.059 <0.001
Random Effects
σ2 3.29
τ00 subID 1.28
τ00 trait 0.12
τ11 subID.WSR.Z 2.58
ρ01 subID 0.07
ICC 0.55
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.057 / 0.573

Covariates and Condition Differences

Similarity-wieghted self-evaluations predict later ingroup endorsements most for those with UCLA comparison and least for those with CSU LA comparison.

m <- glmer( ingChoiceN ~ WSR.Z * outgroup + desirability.Z + ( WSR.Z + desirability.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.154
(1.648 – 2.815)
0.294 5.618 <0.001
WSR Z 1.561
(1.125 – 2.166)
0.261 2.665 0.008
outgroup [CSU LA] 0.981
(0.691 – 1.394)
0.176 -0.106 0.915
outgroup [UCLA] 0.454
(0.316 – 0.651)
0.084 -4.294 <0.001
desirability Z 1.235
(1.163 – 1.312)
0.038 6.856 <0.001
WSR Z * outgroup [CSU LA] 1.114
(0.692 – 1.796)
0.271 0.445 0.656
WSR Z * outgroup [UCLA] 0.822
(0.511 – 1.323)
0.200 -0.806 0.420
Random Effects
σ2 3.29
τ00 subID 0.66
τ00 trait 0.34
τ11 subID.WSR.Z 1.26
τ11 subID.desirability.Z 0.05
τ11 trait.outgroupCSU LA 0.46
τ11 trait.outgroupUCLA 0.65
ρ01 0.07
0.59
-0.89
-0.16
ICC 0.42
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.066 / 0.459
ggpredict(m, c("WSR.Z", "outgroup")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Evaluations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

# fullTest$outgroupP <- factor(fullTest$outgroup, levels = c("CSU LA", "Not UCR", "UCLA"))
# contrasts(fullTest$outgroupP) <- contr.poly(3)
# m <- glmer( ingChoiceN ~ WSR.Z * outgroupP + desirability.Z + evalLOO.Z + ( WSR.Z | subID) + ( outgroupP | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
#                                     optCtrl = list(maxfun = 100000)),
#     nAGQ = 1)
# tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
# ggpredict(m, c("WSR.Z", "outgroup")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Evaluations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

Do cross-validation predictions predict ingroup choices?

No covariates

m <- glmer( ingChoiceN ~ predicted.Z + ( predicted.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.704
(1.478 – 1.966)
0.124 7.329 <0.001
predicted Z 1.581
(1.324 – 1.889)
0.143 5.058 <0.001
Random Effects
σ2 3.29
τ00 subID 0.51
τ00 trait 0.42
τ11 subID.predicted.Z 0.56
τ11 trait.outgroupCSU LA 0.47
τ11 trait.outgroupUCLA 0.69
ρ01 subID 0.28
ρ01 trait.outgroupCSU LA -0.88
ρ01 trait.outgroupUCLA -0.14
ICC 0.31
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.042 / 0.341

With covariates

m <- glmer( ingChoiceN ~ predicted.Z + desirability.Z +  ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.622
(1.410 – 1.866)
0.116 6.774 <0.001
predicted Z 1.545
(1.287 – 1.855)
0.144 4.661 <0.001
desirability Z 1.260
(1.182 – 1.344)
0.041 7.052 <0.001
Random Effects
σ2 3.29
τ00 subID 0.52
τ00 trait 0.13
τ11 subID.predicted.Z 0.64
ρ01 subID 0.28
ICC 0.28
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.051 / 0.320

Covariates and Condition Differences

No effect

m <- glmer( ingChoiceN ~ predicted.Z*outgroup + desirability.Z + ( predicted.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.357
(1.877 – 2.960)
0.274 7.382 <0.001
predicted Z 1.466
(1.164 – 1.846)
0.172 3.254 0.001
outgroup [CSU LA] 0.864
(0.641 – 1.164)
0.132 -0.962 0.336
outgroup [UCLA] 0.404
(0.296 – 0.551)
0.064 -5.720 <0.001
desirability Z 1.197
(1.142 – 1.255)
0.029 7.505 <0.001
predicted Z * outgroup
[CSU LA]
1.136
(0.790 – 1.633)
0.210 0.689 0.491
predicted Z * outgroup
[UCLA]
0.874
(0.610 – 1.252)
0.160 -0.735 0.462
Random Effects
σ2 3.29
τ00 subID 0.43
τ00 trait 0.34
τ11 subID.predicted.Z 0.36
τ11 trait.outgroupCSU LA 0.46
τ11 trait.outgroupUCLA 0.65
ρ01 subID 0.26
ρ01 trait.outgroupCSU LA -0.90
ρ01 trait.outgroupUCLA -0.14
ICC 0.27
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.077 / 0.324
ggpredict(m, c("predicted.Z", "outgroup")) %>% plot(show.title=F) + xlab("Cross-Validated Expectations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.

Does dot-product expected ratings predict ingroup choices?

No covariates

m <- glmer( ingChoiceN ~ er.Z + ( er.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.565
(1.306 – 1.876)
0.145 4.848 <0.001
er Z 1.399
(1.110 – 1.763)
0.165 2.846 0.004
Random Effects
σ2 3.29
τ00 subID 1.01
τ00 trait 0.18
τ11 subID.er.Z 1.95
ρ01 subID -0.03
ICC 0.49
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.017 / 0.497

With covariates

m <- glmer( ingChoiceN ~ er.Z + desirability.Z +  ( er.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.568
(1.314 – 1.871)
0.141 4.984 <0.001
er Z 1.390
(1.103 – 1.751)
0.164 2.794 0.005
desirability Z 1.264
(1.186 – 1.347)
0.041 7.223 <0.001
Random Effects
σ2 3.29
τ00 subID 1.01
τ00 trait 0.13
τ11 subID.er.Z 1.95
ρ01 subID -0.03
ICC 0.48
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.026 / 0.497

Covariates and Condition Differences

Marginall difference in CSU LA compared to Not UCR such that effect of expected ratings is slightly stronger on ingroup choices.

m <- glmer( ingChoiceN ~ er.Z*outgroup + desirability.Z + ( er.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.120
(1.608 – 2.795)
0.299 5.330 <0.001
er Z 1.369
(0.986 – 1.901)
0.229 1.874 0.061
outgroup [CSU LA] 0.949
(0.660 – 1.366)
0.176 -0.280 0.780
outgroup [UCLA] 0.422
(0.291 – 0.613)
0.080 -4.531 <0.001
desirability Z 1.206
(1.150 – 1.264)
0.029 7.755 <0.001
er Z * outgroup [CSU LA] 1.196
(0.746 – 1.919)
0.288 0.743 0.457
er Z * outgroup [UCLA] 0.788
(0.491 – 1.265)
0.190 -0.987 0.324
Random Effects
σ2 3.29
τ00 subID 0.65
τ00 trait 0.34
τ11 subID.er.Z 1.27
τ11 trait.outgroupCSU LA 0.47
τ11 trait.outgroupUCLA 0.65
ρ01 subID 0.02
ρ01 trait.outgroupCSU LA -0.89
ρ01 trait.outgroupUCLA -0.14
ICC 0.42
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.054 / 0.447
ggpredict(m, c("er.Z", "outgroup")) %>% plot(show.title=F) + xlab("Dot-Product Expected Rating") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="er.Z [all]"` to get smooth plots.

Does the linear trend of self-descriptiveness predict ingroup choices?

No covariates

m <- glmer( ingChoiceN ~ slope.Z + ( slope.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.629
(1.357 – 1.956)
0.152 5.239 <0.001
slope Z 1.517
(1.188 – 1.938)
0.189 3.338 0.001
Random Effects
σ2 3.29
τ00 subID 1.01
τ00 trait 0.18
τ11 subID.slope.Z 2.13
ρ01 subID -0.07
ICC 0.50
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.026 / 0.515

With covariates

m <- glmer( ingChoiceN ~ slope.Z + desirability.Z +  ( slope.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.632
(1.365 – 1.951)
0.149 5.382 <0.001
slope Z 1.506
(1.180 – 1.922)
0.187 3.286 0.001
desirability Z 1.262
(1.184 – 1.345)
0.041 7.174 <0.001
Random Effects
σ2 3.29
τ00 subID 1.00
τ00 trait 0.13
τ11 subID.slope.Z 2.13
ρ01 subID -0.07
ICC 0.50
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.034 / 0.514

Covariates and Condition Differences

Effect of self-descriptiveness trend is greatest for UCLA and weakest for CSU LA

m <- glmer( ingChoiceN ~ slope.Z*outgroup + desirability.Z + ( slope.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.230
(1.699 – 2.925)
0.309 5.788 <0.001
slope Z 1.509
(1.087 – 2.095)
0.252 2.458 0.014
outgroup [CSU LA] 0.918
(0.643 – 1.309)
0.166 -0.474 0.635
outgroup [UCLA] 0.408
(0.283 – 0.587)
0.076 -4.827 <0.001
desirability Z 1.205
(1.149 – 1.264)
0.029 7.729 <0.001
slope Z * outgroup [CSU
LA]
1.021
(0.635 – 1.644)
0.248 0.087 0.931
slope Z * outgroup [UCLA] 0.782
(0.486 – 1.259)
0.190 -1.012 0.312
Random Effects
σ2 3.29
τ00 subID 0.59
τ00 trait 0.34
τ11 subID.slope.Z 1.24
τ11 trait.outgroupCSU LA 0.46
τ11 trait.outgroupUCLA 0.65
ρ01 subID -0.02
ρ01 trait.outgroupCSU LA -0.89
ρ01 trait.outgroupUCLA -0.15
ICC 0.41
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.061 / 0.443
ggpredict(m, c("slope.Z", "outgroup")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Evaluations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.

Do the neighboring self-evaluations predict ingroup choices?

All neighbors

No covariates

m <- glmer( ingChoiceN ~ neighAveAllSE.Z + ( neighAveAllSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.690
(1.473 – 1.939)
0.118 7.491 <0.001
neighAveAllSE Z 1.236
(1.115 – 1.369)
0.065 4.052 <0.001
Random Effects
σ2 3.29
τ00 subID 0.62
τ00 trait 0.18
τ11 subID.neighAveAllSE.Z 0.36
ρ01 subID -0.07
ICC 0.26
N subID 200
N trait 148
Observations 29329
Marginal R2 / Conditional R2 0.010 / 0.269

With covariates

m <- glmer( ingChoiceN ~ neighAveAllSE.Z + desirability.Z +  ( neighAveAllSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.692
(1.483 – 1.930)
0.114 7.807 <0.001
neighAveAllSE Z 1.232
(1.112 – 1.365)
0.064 4.001 <0.001
desirability Z 1.265
(1.187 – 1.347)
0.041 7.281 <0.001
Random Effects
σ2 3.29
τ00 subID 0.62
τ00 trait 0.13
τ11 subID.neighAveAllSE.Z 0.36
ρ01 subID -0.07
ICC 0.25
N subID 200
N trait 148
Observations 29329
Marginal R2 / Conditional R2 0.023 / 0.269

Covariates and Condition Differences

Effect of neighboring self-evaluations on ingroup choices is strongest for UCLA and weakest for CSU LA

m <- glmer( ingChoiceN ~ neighAveAllSE.Z*outgroup + desirability.Z + ( neighAveAllSE.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.463
(1.991 – 3.047)
0.267 8.310 <0.001
neighAveAllSE Z 1.225
(1.050 – 1.429)
0.096 2.575 0.010
outgroup [CSU LA] 0.814
(0.612 – 1.082)
0.118 -1.415 0.157
outgroup [UCLA] 0.395
(0.294 – 0.530)
0.059 -6.169 <0.001
desirability Z 1.210
(1.154 – 1.269)
0.029 7.899 <0.001
neighAveAllSE Z *
outgroup [CSU LA]
1.019
(0.819 – 1.267)
0.113 0.168 0.867
neighAveAllSE Z *
outgroup [UCLA]
0.893
(0.716 – 1.115)
0.101 -0.999 0.318
Random Effects
σ2 3.29
τ00 subID 0.50
τ00 trait 0.33
τ11 subID.neighAveAllSE.Z 0.25
τ11 trait.outgroupCSU LA 0.45
τ11 trait.outgroupUCLA 0.65
ρ01 subID -0.02
ρ01 trait.outgroupCSU LA -0.89
ρ01 trait.outgroupUCLA -0.16
ICC 0.26
N subID 200
N trait 148
Observations 29329
Marginal R2 / Conditional R2 0.052 / 0.299
ggpredict(m, c("neighAveAllSE.Z", "outgroup")) %>% plot(show.title=F) + xlab("Neighbors' Average Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="neighAveAllSE.Z [all]"` to get smooth plots.

All neighbors

No covariates

m <- glmer( ingChoiceN ~ neighAveOutSE.Z + ( neighAveOutSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.710
(1.493 – 1.958)
0.118 7.768 <0.001
neighAveOutSE Z 1.197
(1.100 – 1.302)
0.051 4.174 <0.001
Random Effects
σ2 3.29
τ00 subID 0.63
τ00 trait 0.18
τ11 subID.neighAveOutSE.Z 0.24
ρ01 subID -0.08
ICC 0.24
N subID 200
N trait 147
Observations 29060
Marginal R2 / Conditional R2 0.007 / 0.248

With covariates

m <- glmer( ingChoiceN ~ neighAveOutSE.Z + desirability.Z +  ( neighAveOutSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.710
(1.502 – 1.947)
0.113 8.100 <0.001
neighAveOutSE Z 1.195
(1.098 – 1.300)
0.051 4.145 <0.001
desirability Z 1.265
(1.188 – 1.347)
0.041 7.330 <0.001
Random Effects
σ2 3.29
τ00 subID 0.63
τ00 trait 0.13
τ11 subID.neighAveOutSE.Z 0.24
ρ01 subID -0.08
ICC 0.23
N subID 200
N trait 147
Observations 29060
Marginal R2 / Conditional R2 0.021 / 0.249

Covariates and Condition Differences

Effect of neighboring self-evaluations on ingroup choices is strongest for UCLA and weakest for CSU LA

m <- glmer( ingChoiceN ~ neighAveOutSE.Z*outgroup + desirability.Z + ( neighAveOutSE.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.523
(2.048 – 3.109)
0.269 8.689 <0.001
neighAveOutSE Z 1.151
(1.013 – 1.307)
0.075 2.161 0.031
outgroup [CSU LA] 0.804
(0.606 – 1.066)
0.116 -1.516 0.130
outgroup [UCLA] 0.382
(0.285 – 0.512)
0.057 -6.459 <0.001
desirability Z 1.209
(1.154 – 1.268)
0.029 7.894 <0.001
neighAveOutSE Z *
outgroup [CSU LA]
1.086
(0.909 – 1.298)
0.099 0.908 0.364
neighAveOutSE Z *
outgroup [UCLA]
0.943
(0.786 – 1.130)
0.087 -0.637 0.524
Random Effects
σ2 3.29
τ00 subID 0.51
τ00 trait 0.33
τ11 subID.neighAveOutSE.Z 0.16
τ11 trait.outgroupCSU LA 0.45
τ11 trait.outgroupUCLA 0.63
ρ01 subID -0.05
ρ01 trait.outgroupCSU LA -0.89
ρ01 trait.outgroupUCLA -0.15
ICC 0.25
N subID 200
N trait 147
Observations 29060
Marginal R2 / Conditional R2 0.052 / 0.287
ggpredict(m, c("neighAveOutSE.Z", "outgroup")) %>% plot(show.title=F) + xlab("Outwards Neighbors' Average Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="neighAveOutSE.Z [all]"` to get smooth plots.

Indegree neighbors

No covariates

m <- glmer( ingChoiceN ~ neighAveInSE.Z + ( neighAveInSE.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.777
(1.549 – 2.038)
0.124 8.208 <0.001
neighAveInSE Z 1.148
(1.064 – 1.238)
0.044 3.575 <0.001
Random Effects
σ2 3.29
τ00 subID 0.64
τ00 trait 0.43
τ11 subID.neighAveInSE.Z 0.16
τ11 trait.outgroupCSU LA 0.46
τ11 trait.outgroupUCLA 0.70
ρ01 subID 0.15
ρ01 trait.outgroupCSU LA -0.87
ρ01 trait.outgroupUCLA -0.15
ICC 0.27
N subID 200
N trait 148
Observations 29282
Marginal R2 / Conditional R2 0.004 / 0.276

With covariates

m <- glmer( ingChoiceN ~ neighAveInSE.Z + desirability.Z +  ( neighAveInSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.699
(1.488 – 1.940)
0.115 7.839 <0.001
neighAveInSE Z 1.158
(1.068 – 1.254)
0.047 3.569 <0.001
desirability Z 1.264
(1.186 – 1.348)
0.041 7.176 <0.001
Random Effects
σ2 3.29
τ00 subID 0.65
τ00 trait 0.13
τ11 subID.neighAveInSE.Z 0.20
ρ01 subID 0.06
ICC 0.23
N subID 200
N trait 148
Observations 29282
Marginal R2 / Conditional R2 0.019 / 0.246

Covariates and Condition Differences

Effect of neighboring self-evaluations on ingroup choices is strongest for UCLA and weakest for CSU LA

m <- glmer( ingChoiceN ~ neighAveInSE.Z*outgroup + desirability.Z + ( neighAveInSE.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.498
(2.018 – 3.093)
0.272 8.402 <0.001
neighAveInSE Z 1.152
(1.014 – 1.309)
0.075 2.167 0.030
outgroup [CSU LA] 0.820
(0.614 – 1.094)
0.121 -1.352 0.176
outgroup [UCLA] 0.384
(0.285 – 0.517)
0.058 -6.294 <0.001
desirability Z 1.206
(1.150 – 1.264)
0.029 7.759 <0.001
neighAveInSE Z * outgroup
[CSU LA]
1.015
(0.849 – 1.214)
0.093 0.168 0.866
neighAveInSE Z * outgroup
[UCLA]
0.941
(0.785 – 1.129)
0.087 -0.652 0.514
Random Effects
σ2 3.29
τ00 subID 0.54
τ00 trait 0.34
τ11 subID.neighAveInSE.Z 0.16
τ11 trait.outgroupCSU LA 0.46
τ11 trait.outgroupUCLA 0.65
ρ01 subID 0.12
ρ01 trait.outgroupCSU LA -0.90
ρ01 trait.outgroupUCLA -0.16
ICC 0.25
N subID 200
N trait 148
Observations 29282
Marginal R2 / Conditional R2 0.050 / 0.291
ggpredict(m, c("neighAveInSE.Z", "outgroup")) %>% plot(show.title=F) + xlab("Inwards Neighbors' Average Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="neighAveInSE.Z [all]"` to get smooth plots.

Do people self-anchor more for higher indegree traits?

Similarity-Weighted Self-Evaluations

m <- glmer( ingChoiceN ~ WSR.Z * inDegree.Z + desirability.Z + ( WSR.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.664
(1.371 – 2.020)
0.164 5.153 <0.001
WSR Z 1.858
(1.423 – 2.426)
0.253 4.554 <0.001
inDegree Z 1.003
(0.941 – 1.069)
0.033 0.092 0.927
desirability Z 1.249
(1.173 – 1.330)
0.040 6.938 <0.001
WSR Z * inDegree Z 1.037
(1.006 – 1.068)
0.016 2.345 0.019
Random Effects
σ2 3.29
τ00 subID 1.27
τ00 trait 0.12
τ11 subID.WSR.Z 2.61
τ11 subID.inDegree.Z 0.01
ρ01 subID.WSR.Z 0.07
ρ01 subID.inDegree.Z 0.18
ICC 0.55
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.058 / 0.575
ggpredict(m, c("WSR.Z", "inDegree.Z")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * inDegree.Z + desirability.Z + ( predicted.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.649
(1.436 – 1.894)
0.116 7.087 <0.001
predicted Z 1.550
(1.292 – 1.860)
0.144 4.710 <0.001
inDegree Z 1.001
(0.936 – 1.071)
0.034 0.039 0.969
desirability Z 1.261
(1.181 – 1.347)
0.042 6.951 <0.001
predicted Z * inDegree Z 1.034
(1.004 – 1.064)
0.016 2.194 0.028
Random Effects
σ2 3.29
τ00 subID 0.51
τ00 trait 0.13
τ11 subID.predicted.Z 0.62
τ11 subID.inDegree.Z 0.01
ρ01 subID.predicted.Z 0.28
ρ01 subID.inDegree.Z 0.33
ICC 0.28
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.053 / 0.316

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * inDegree.Z + desirability.Z + ( er.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.585
(1.328 – 1.891)
0.143 5.108 <0.001
er Z 1.400
(1.111 – 1.765)
0.165 2.849 0.004
inDegree Z 1.009
(0.944 – 1.078)
0.034 0.261 0.794
desirability Z 1.263
(1.184 – 1.347)
0.042 7.066 <0.001
er Z * inDegree Z 1.036
(1.006 – 1.067)
0.016 2.355 0.019
Random Effects
σ2 3.29
τ00 subID 1.00
τ00 trait 0.13
τ11 subID.er.Z 1.96
τ11 subID.inDegree.Z 0.01
ρ01 subID.er.Z -0.02
ρ01 subID.inDegree.Z 0.16
ICC 0.48
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.027 / 0.498

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * inDegree.Z + desirability.Z + ( slope.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.653
(1.384 – 1.976)
0.150 5.532 <0.001
slope Z 1.514
(1.185 – 1.934)
0.189 3.318 0.001
inDegree Z 1.009
(0.944 – 1.078)
0.034 0.260 0.795
desirability Z 1.261
(1.182 – 1.345)
0.042 7.021 <0.001
slope Z * inDegree Z 1.038
(1.008 – 1.070)
0.016 2.451 0.014
Random Effects
σ2 3.29
τ00 subID 1.00
τ00 trait 0.13
τ11 subID.slope.Z 2.14
τ11 subID.inDegree.Z 0.01
ρ01 subID.slope.Z -0.06
ρ01 subID.inDegree.Z 0.22
ICC 0.50
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.035 / 0.515

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * inDegree.Z + desirability.Z + ( neighAveAllSE.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.697
(1.487 – 1.936)
0.114 7.864 <0.001
neighAveAllSE Z 1.249
(1.127 – 1.384)
0.065 4.241 <0.001
inDegree Z 1.012
(0.948 – 1.081)
0.034 0.365 0.715
desirability Z 1.262
(1.184 – 1.346)
0.041 7.121 <0.001
neighAveAllSE Z *
inDegree Z
1.043
(1.012 – 1.074)
0.016 2.718 0.007
Random Effects
σ2 3.29
τ00 subID 0.62
τ00 trait 0.13
τ11 subID.neighAveAllSE.Z 0.35
τ11 subID.inDegree.Z 0.01
ρ01 subID.neighAveAllSE.Z -0.07
ρ01 subID.inDegree.Z 0.35
ICC 0.25
N subID 200
N trait 148
Observations 29329
Marginal R2 / Conditional R2 0.025 / 0.271

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * inDegree.Z + desirability.Z + ( neighAveOutSE.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.713
(1.504 – 1.950)
0.114 8.117 <0.001
neighAveOutSE Z 1.201
(1.104 – 1.306)
0.052 4.249 <0.001
inDegree Z 1.011
(0.947 – 1.080)
0.034 0.337 0.736
desirability Z 1.263
(1.185 – 1.347)
0.041 7.170 <0.001
neighAveOutSE Z *
inDegree Z
1.029
(0.999 – 1.059)
0.015 1.891 0.059
Random Effects
σ2 3.29
τ00 subID 0.63
τ00 trait 0.13
τ11 subID.neighAveOutSE.Z 0.24
τ11 subID.inDegree.Z 0.01
ρ01 subID.neighAveOutSE.Z -0.08
ρ01 subID.inDegree.Z 0.32
ICC 0.23
N subID 200
N trait 147
Observations 29060
Marginal R2 / Conditional R2 0.022 / 0.251

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * inDegree.Z + desirability.Z + ( neighAveInSE.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.703
(1.492 – 1.945)
0.115 7.879 <0.001
neighAveInSE Z 1.186
(1.093 – 1.287)
0.049 4.099 <0.001
inDegree Z 1.018
(0.953 – 1.088)
0.034 0.535 0.593
desirability Z 1.260
(1.181 – 1.344)
0.042 6.986 <0.001
neighAveInSE Z * inDegree
Z
1.051
(1.019 – 1.084)
0.017 3.142 0.002
Random Effects
σ2 3.29
τ00 subID 0.66
τ00 trait 0.13
τ11 subID.neighAveInSE.Z 0.20
τ11 subID.inDegree.Z 0.01
ρ01 subID.neighAveInSE.Z 0.06
ρ01 subID.inDegree.Z 0.34
ICC 0.23
N subID 200
N trait 148
Observations 29282
Marginal R2 / Conditional R2 0.021 / 0.248

Do people self-anchor more for higher outdegree traits?

Similarity-Weighted Self-Evaluations

Similarity-weighted self-descriptiveness more predictive of ingroup choices for higher outdegree traits

m <- glmer( ingChoiceN ~ WSR.Z * outDegree.Z + desirability.Z + ( WSR.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.689
(1.400 – 2.036)
0.161 5.481 <0.001
WSR Z 1.818
(1.409 – 2.345)
0.236 4.595 <0.001
outDegree Z 1.097
(1.017 – 1.182)
0.042 2.405 0.016
desirability Z 1.215
(1.134 – 1.301)
0.043 5.528 <0.001
WSR Z * outDegree Z 1.076
(1.034 – 1.121)
0.022 3.567 <0.001
Random Effects
σ2 3.29
τ00 subID 1.19
τ00 trait 0.12
τ11 subID.WSR.Z 2.31
τ11 subID.outDegree.Z 0.04
ρ01 subID.WSR.Z 0.04
ρ01 subID.outDegree.Z 0.48
ICC 0.53
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.059 / 0.554
ggpredict(m, c("WSR.Z", "outDegree.Z")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * outDegree.Z + desirability.Z + ( predicted.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.699
(1.485 – 1.943)
0.116 7.740 <0.001
predicted Z 1.501
(1.297 – 1.738)
0.112 5.432 <0.001
outDegree Z 1.103
(1.018 – 1.195)
0.045 2.399 0.016
desirability Z 1.231
(1.145 – 1.323)
0.045 5.645 <0.001
predicted Z * outDegree Z 1.086
(1.041 – 1.134)
0.024 3.771 <0.001
Random Effects
σ2 3.29
τ00 subID 0.59
τ00 trait 0.13
τ11 subID.predicted.Z 0.23
τ11 subID.outDegree.Z 0.05
ρ01 subID.predicted.Z 0.13
ρ01 subID.outDegree.Z 0.86
ICC 0.23
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.056 / 0.275

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * outDegree.Z + desirability.Z + ( er.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.616
(1.362 – 1.917)
0.141 5.507 <0.001
er Z 1.389
(1.114 – 1.731)
0.156 2.925 0.003
outDegree Z 1.101
(1.019 – 1.190)
0.044 2.432 0.015
desirability Z 1.228
(1.144 – 1.318)
0.045 5.657 <0.001
er Z * outDegree Z 1.066
(1.024 – 1.109)
0.022 3.141 0.002
Random Effects
σ2 3.29
τ00 subID 0.94
τ00 trait 0.13
τ11 subID.er.Z 1.72
τ11 subID.outDegree.Z 0.04
ρ01 subID.er.Z -0.05
ρ01 subID.outDegree.Z 0.55
ICC 0.46
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.030 / 0.478

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * outDegree.Z + desirability.Z + ( slope.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.677
(1.412 – 1.993)
0.148 5.879 <0.001
slope Z 1.497
(1.186 – 1.890)
0.178 3.399 0.001
outDegree Z 1.102
(1.020 – 1.191)
0.044 2.453 0.014
desirability Z 1.225
(1.141 – 1.315)
0.044 5.615 <0.001
slope Z * outDegree Z 1.074
(1.031 – 1.118)
0.022 3.456 0.001
Random Effects
σ2 3.29
τ00 subID 0.94
τ00 trait 0.13
τ11 subID.slope.Z 1.88
τ11 subID.outDegree.Z 0.04
ρ01 subID.slope.Z -0.10
ρ01 subID.outDegree.Z 0.56
ICC 0.48
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.038 / 0.495

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * outDegree.Z + desirability.Z + ( neighAveAllSE.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.720
(1.506 – 1.965)
0.117 7.997 <0.001
neighAveAllSE Z 1.267
(1.147 – 1.401)
0.065 4.644 <0.001
outDegree Z 1.121
(1.038 – 1.212)
0.044 2.901 0.004
desirability Z 1.222
(1.139 – 1.311)
0.044 5.614 <0.001
neighAveAllSE Z *
outDegree Z
1.073
(1.033 – 1.115)
0.021 3.598 <0.001
Random Effects
σ2 3.29
τ00 subID 0.65
τ00 trait 0.12
τ11 subID.neighAveAllSE.Z 0.31
τ11 subID.outDegree.Z 0.05
ρ01 subID.neighAveAllSE.Z -0.05
ρ01 subID.outDegree.Z 0.76
ICC 0.26
N subID 200
N trait 148
Observations 29329
Marginal R2 / Conditional R2 0.030 / 0.279

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * outDegree.Z + desirability.Z + ( neighAveOutSE.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.738
(1.525 – 1.982)
0.116 8.273 <0.001
neighAveOutSE Z 1.234
(1.133 – 1.343)
0.054 4.845 <0.001
outDegree Z 1.109
(1.025 – 1.199)
0.044 2.590 0.010
desirability Z 1.228
(1.145 – 1.317)
0.044 5.769 <0.001
neighAveOutSE Z *
outDegree Z
1.067
(1.026 – 1.110)
0.021 3.221 0.001
Random Effects
σ2 3.29
τ00 subID 0.65
τ00 trait 0.12
τ11 subID.neighAveOutSE.Z 0.22
τ11 subID.outDegree.Z 0.05
ρ01 subID.neighAveOutSE.Z -0.09
ρ01 subID.outDegree.Z 0.78
ICC 0.24
N subID 200
N trait 147
Observations 29060
Marginal R2 / Conditional R2 0.028 / 0.262

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * outDegree.Z + desirability.Z + ( neighAveInSE.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.731
(1.512 – 1.981)
0.119 7.969 <0.001
neighAveInSE Z 1.169
(1.081 – 1.265)
0.047 3.895 <0.001
outDegree Z 1.120
(1.035 – 1.212)
0.045 2.802 0.005
desirability Z 1.224
(1.140 – 1.314)
0.044 5.557 <0.001
neighAveInSE Z *
outDegree Z
1.053
(1.016 – 1.091)
0.019 2.857 0.004
Random Effects
σ2 3.29
τ00 subID 0.69
τ00 trait 0.13
τ11 subID.neighAveInSE.Z 0.18
τ11 subID.outDegree.Z 0.05
ρ01 subID.neighAveInSE.Z 0.06
ρ01 subID.outDegree.Z 0.81
ICC 0.24
N subID 200
N trait 148
Observations 29282
Marginal R2 / Conditional R2 0.024 / 0.262

Do cross-validated similarity * self-evaluation predictions predict ingroup choices, regardless of whether it was seen prior or not?

Similarity-Weighted Self-Evaluations

m <- glmer( ingChoiceN ~ WSR.Z * novel + desirability.Z + ( WSR.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.651
(1.360 – 2.004)
0.163 5.065 <0.001
WSR Z 1.874
(1.436 – 2.446)
0.255 4.627 <0.001
novel [Held Out] 0.983
(0.932 – 1.036)
0.027 -0.643 0.520
desirability Z 1.249
(1.174 – 1.329)
0.039 7.064 <0.001
WSR Z * novel [Held Out] 0.965
(0.913 – 1.019)
0.027 -1.287 0.198
Random Effects
σ2 3.29
τ00 subID 1.26
τ00 trait 0.12
τ11 subID.WSR.Z 2.59
τ11 subID.novelHeld Out 0.00
ρ01 subID.WSR.Z 0.06
ρ01 subID.novelHeld Out 0.80
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.117 / NA
ggpredict(m, c("WSR.Z", "novel")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Moderated by outgroup comparison

m <- glmer( ingChoiceN ~ WSR.Z * novel * outgroup + desirability.Z + ( WSR.Z + novel | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.199
(1.637 – 2.953)
0.331 5.233 <0.001
WSR Z 1.705
(1.190 – 2.443)
0.313 2.905 0.004
novel [Held Out] 0.962
(0.872 – 1.062)
0.048 -0.769 0.442
outgroup [CSU LA] 0.917
(0.621 – 1.353)
0.182 -0.438 0.661
outgroup [UCLA] 0.444
(0.298 – 0.661)
0.090 -4.002 <0.001
desirability Z 1.197
(1.142 – 1.254)
0.029 7.514 <0.001
WSR Z * novel [Held Out] 0.960
(0.882 – 1.044)
0.041 -0.959 0.338
WSR Z * outgroup [CSU LA] 1.086
(0.646 – 1.824)
0.287 0.310 0.756
WSR Z * outgroup [UCLA] 0.834
(0.494 – 1.407)
0.223 -0.681 0.496
novel [Held Out] *
outgroup [CSU LA]
0.986
(0.863 – 1.126)
0.067 -0.214 0.831
novel [Held Out] *
outgroup [UCLA]
1.095
(0.955 – 1.256)
0.076 1.302 0.193
(WSR Z * novel [Held
Out]) * outgroup [CSU LA]
0.985
(0.862 – 1.125)
0.067 -0.224 0.823
(WSR Z * novel [Held
Out]) * outgroup [UCLA]
1.058
(0.926 – 1.210)
0.072 0.830 0.407
Random Effects
σ2 3.29
τ00 subID 0.75
τ00 trait 0.34
τ11 subID.WSR.Z 1.56
τ11 subID.novelHeld Out 0.00
τ11 trait.outgroupCSU LA 0.47
τ11 trait.outgroupUCLA 0.63
ρ01 0.11
0.59
-0.89
-0.16
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.119 / NA
ggpredict(m, c("WSR.Z", "novel", "outgroup")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * novel + desirability.Z + ( predicted.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.632
(1.416 – 1.880)
0.118 6.767 <0.001
predicted Z 1.573
(1.309 – 1.891)
0.148 4.822 <0.001
novel [Held Out] 0.988
(0.937 – 1.042)
0.027 -0.448 0.654
desirability Z 1.260
(1.182 – 1.344)
0.041 7.055 <0.001
predicted Z * novel [Held
Out]
0.962
(0.912 – 1.016)
0.026 -1.397 0.162
Random Effects
σ2 3.29
τ00 subID 0.53
τ00 trait 0.13
τ11 subID.predicted.Z 0.64
τ11 subID.novelHeld Out 0.00
ρ01 subID.predicted.Z 0.25
ρ01 subID.novelHeld Out -0.39
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.071 / NA

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * novel + desirability.Z + ( er.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.581
(1.322 – 1.891)
0.144 5.018 <0.001
er Z 1.409
(1.117 – 1.776)
0.167 2.895 0.004
novel [Held Out] 0.979
(0.928 – 1.032)
0.026 -0.791 0.429
desirability Z 1.264
(1.187 – 1.347)
0.041 7.229 <0.001
er Z * novel [Held Out] 0.962
(0.912 – 1.015)
0.026 -1.409 0.159
Random Effects
σ2 3.29
τ00 subID 1.02
τ00 trait 0.13
τ11 subID.er.Z 1.95
τ11 subID.novelHeld Out 0.00
ρ01 subID.er.Z -0.01
ρ01 subID.novelHeld Out -0.42
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.048 / NA

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * novel + desirability.Z + ( slope.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.645
(1.373 – 1.970)
0.151 5.406 <0.001
slope Z 1.526
(1.194 – 1.950)
0.191 3.378 0.001
novel [Held Out] 0.980
(0.930 – 1.033)
0.026 -0.743 0.457
desirability Z 1.262
(1.184 – 1.345)
0.041 7.180 <0.001
slope Z * novel [Held
Out]
0.960
(0.910 – 1.013)
0.026 -1.479 0.139
Random Effects
σ2 3.29
τ00 subID 1.02
τ00 trait 0.13
τ11 subID.slope.Z 2.13
τ11 subID.novelHeld Out 0.00
ρ01 subID.slope.Z -0.06
ρ01 subID.novelHeld Out -0.50
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.064 / NA

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * novel + desirability.Z + ( neighAveAllSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.701
(1.487 – 1.945)
0.116 7.756 <0.001
neighAveAllSE Z 1.241
(1.118 – 1.378)
0.066 4.052 <0.001
novel [Held Out] 0.988
(0.937 – 1.042)
0.027 -0.450 0.653
desirability Z 1.265
(1.187 – 1.347)
0.041 7.282 <0.001
neighAveAllSE Z * novel
[Held Out]
0.983
(0.931 – 1.037)
0.027 -0.629 0.530
Random Effects
σ2 3.29
τ00 subID 0.63
τ00 trait 0.13
τ11 subID.neighAveAllSE.Z 0.36
τ11 subID.novelHeld Out 0.00
ρ01 subID.neighAveAllSE.Z -0.08
ρ01 subID.novelHeld Out -0.56
N subID 200
N trait 148
Observations 29329
Marginal R2 / Conditional R2 0.031 / NA

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * novel + desirability.Z + ( neighAveOutSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.724
(1.509 – 1.968)
0.117 8.038 <0.001
neighAveOutSE Z 1.204
(1.104 – 1.314)
0.053 4.190 <0.001
novel [Held Out] 0.981
(0.930 – 1.035)
0.027 -0.687 0.492
desirability Z 1.265
(1.188 – 1.347)
0.041 7.333 <0.001
neighAveOutSE Z * novel
[Held Out]
0.980
(0.928 – 1.035)
0.027 -0.732 0.464
Random Effects
σ2 3.29
τ00 subID 0.64
τ00 trait 0.13
τ11 subID.neighAveOutSE.Z 0.24
τ11 subID.novelHeld Out 0.00
ρ01 subID.neighAveOutSE.Z -0.07
ρ01 subID.novelHeld Out -1.00
N subID 200
N trait 147
Observations 29060
Marginal R2 / Conditional R2 0.028 / NA

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * novel + desirability.Z + ( neighAveInSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.711
(1.495 – 1.959)
0.118 7.778 <0.001
neighAveInSE Z 1.168
(1.074 – 1.269)
0.050 3.647 <0.001
novel [Held Out] 0.984
(0.933 – 1.038)
0.027 -0.581 0.561
desirability Z 1.264
(1.186 – 1.348)
0.041 7.177 <0.001
neighAveInSE Z * novel
[Held Out]
0.980
(0.928 – 1.034)
0.027 -0.749 0.454
Random Effects
σ2 3.29
τ00 subID 0.67
τ00 trait 0.13
τ11 subID.neighAveInSE.Z 0.20
τ11 subID.novelHeld Out 0.00
ρ01 subID.neighAveInSE.Z 0.05
ρ01 subID.novelHeld Out -0.61
N subID 200
N trait 148
Observations 29282
Marginal R2 / Conditional R2 0.024 / NA

Does generalization depend on outdegree?

Similarity-Weighted Self-Evaluations

m <- glmer( ingChoiceN ~ WSR.Z * novel * outDegree.Z + desirability.Z + ( WSR.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.674
(1.379 – 2.031)
0.165 5.213 <0.001
WSR Z 1.895
(1.452 – 2.472)
0.257 4.710 <0.001
novel [Held Out] 0.984
(0.933 – 1.038)
0.027 -0.595 0.552
outDegree Z 1.058
(0.985 – 1.137)
0.039 1.542 0.123
desirability Z 1.213
(1.133 – 1.299)
0.042 5.549 <0.001
WSR Z * novel [Held Out] 0.963
(0.912 – 1.018)
0.027 -1.326 0.185
WSR Z * outDegree Z 1.070
(1.033 – 1.109)
0.019 3.790 <0.001
novel [Held Out] *
outDegree Z
1.025
(0.971 – 1.082)
0.028 0.896 0.370
(WSR Z * novel [Held
Out]) * outDegree Z
0.962
(0.910 – 1.016)
0.027 -1.388 0.165
Random Effects
σ2 3.29
τ00 subID 1.26
τ00 trait 0.12
τ11 subID.WSR.Z 2.58
τ11 subID.novelHeld Out 0.00
ρ01 subID.WSR.Z 0.06
ρ01 subID.novelHeld Out 0.81
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.122 / NA
ggpredict(m, c("WSR.Z", "novel")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * novel * outDegree.Z + desirability.Z + ( predicted.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.652
(1.435 – 1.902)
0.119 6.975 <0.001
predicted Z 1.565
(1.303 – 1.881)
0.147 4.783 <0.001
novel [Held Out] 0.989
(0.938 – 1.043)
0.027 -0.401 0.689
outDegree Z 1.049
(0.973 – 1.131)
0.040 1.254 0.210
desirability Z 1.230
(1.145 – 1.321)
0.045 5.687 <0.001
predicted Z * novel [Held
Out]
0.961
(0.911 – 1.014)
0.026 -1.450 0.147
predicted Z * outDegree Z 1.070
(1.034 – 1.108)
0.019 3.827 <0.001
novel [Held Out] *
outDegree Z
1.019
(0.967 – 1.075)
0.028 0.709 0.478
(predicted Z * novel
[Held Out]) * outDegree Z
0.961
(0.910 – 1.014)
0.027 -1.449 0.147
Random Effects
σ2 3.29
τ00 subID 0.52
τ00 trait 0.13
τ11 subID.predicted.Z 0.64
τ11 subID.novelHeld Out 0.00
ρ01 subID.predicted.Z 0.27
ρ01 subID.novelHeld Out -0.44
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.072 / NA

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * novel * outDegree.Z + desirability.Z + ( er.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.598
(1.337 – 1.909)
0.145 5.150 <0.001
er Z 1.423
(1.129 – 1.794)
0.168 2.986 0.003
novel [Held Out] 0.980
(0.929 – 1.033)
0.027 -0.750 0.453
outDegree Z 1.062
(0.986 – 1.143)
0.040 1.591 0.112
desirability Z 1.227
(1.144 – 1.316)
0.044 5.695 <0.001
er Z * novel [Held Out] 0.960
(0.910 – 1.013)
0.026 -1.475 0.140
er Z * outDegree Z 1.071
(1.034 – 1.109)
0.019 3.852 <0.001
novel [Held Out] *
outDegree Z
1.022
(0.968 – 1.078)
0.028 0.782 0.434
(er Z * novel [Held Out])
* outDegree Z
0.960
(0.908 – 1.014)
0.027 -1.465 0.143
Random Effects
σ2 3.29
τ00 subID 1.02
τ00 trait 0.13
τ11 subID.er.Z 1.94
τ11 subID.novelHeld Out 0.00
ρ01 subID.er.Z -0.01
ρ01 subID.novelHeld Out -0.40
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.052 / NA

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * novel * outDegree.Z + desirability.Z + ( slope.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.664
(1.390 – 1.992)
0.153 5.553 <0.001
slope Z 1.540
(1.206 – 1.968)
0.192 3.458 0.001
novel [Held Out] 0.981
(0.930 – 1.035)
0.027 -0.703 0.482
outDegree Z 1.063
(0.987 – 1.144)
0.040 1.623 0.105
desirability Z 1.224
(1.141 – 1.313)
0.044 5.646 <0.001
slope Z * novel [Held
Out]
0.958
(0.908 – 1.012)
0.026 -1.534 0.125
slope Z * outDegree Z 1.071
(1.034 – 1.109)
0.019 3.826 <0.001
novel [Held Out] *
outDegree Z
1.020
(0.967 – 1.076)
0.028 0.722 0.470
(slope Z * novel [Held
Out]) * outDegree Z
0.962
(0.910 – 1.016)
0.027 -1.379 0.168
Random Effects
σ2 3.29
τ00 subID 1.01
τ00 trait 0.12
τ11 subID.slope.Z 2.12
τ11 subID.novelHeld Out 0.00
ρ01 subID.slope.Z -0.05
ρ01 subID.novelHeld Out -0.49
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.068 / NA

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * novel * outDegree.Z + desirability.Z + ( neighAveAllSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.702
(1.490 – 1.945)
0.116 7.818 <0.001
neighAveAllSE Z 1.279
(1.151 – 1.420)
0.068 4.594 <0.001
novel [Held Out] 0.987
(0.936 – 1.041)
0.027 -0.475 0.635
outDegree Z 1.072
(0.997 – 1.153)
0.040 1.869 0.062
desirability Z 1.222
(1.140 – 1.309)
0.043 5.651 <0.001
neighAveAllSE Z * novel
[Held Out]
0.975
(0.923 – 1.029)
0.027 -0.920 0.357
neighAveAllSE Z *
outDegree Z
1.089
(1.050 – 1.130)
0.020 4.593 <0.001
novel [Held Out] *
outDegree Z
1.013
(0.960 – 1.069)
0.028 0.470 0.638
(neighAveAllSE Z * novel
[Held Out]) * outDegree Z
0.946
(0.894 – 1.001)
0.027 -1.922 0.055
Random Effects
σ2 3.29
τ00 subID 0.63
τ00 trait 0.12
τ11 subID.neighAveAllSE.Z 0.36
τ11 subID.novelHeld Out 0.00
ρ01 subID.neighAveAllSE.Z -0.07
ρ01 subID.novelHeld Out -0.57
N subID 200
N trait 148
Observations 29329
Marginal R2 / Conditional R2 0.036 / NA

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * novel * outDegree.Z + desirability.Z + ( neighAveOutSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.722
(1.510 – 1.964)
0.116 8.094 <0.001
neighAveOutSE Z 1.252
(1.146 – 1.368)
0.056 4.989 <0.001
novel [Held Out] 0.982
(0.931 – 1.036)
0.027 -0.662 0.508
outDegree Z 1.054
(0.979 – 1.134)
0.039 1.395 0.163
desirability Z 1.228
(1.146 – 1.315)
0.043 5.814 <0.001
neighAveOutSE Z * novel
[Held Out]
0.970
(0.917 – 1.025)
0.028 -1.085 0.278
neighAveOutSE Z *
outDegree Z
1.089
(1.048 – 1.132)
0.021 4.375 <0.001
novel [Held Out] *
outDegree Z
1.024
(0.970 – 1.081)
0.028 0.860 0.390
(neighAveOutSE Z * novel
[Held Out]) * outDegree Z
0.958
(0.904 – 1.016)
0.029 -1.419 0.156
Random Effects
σ2 3.29
τ00 subID 0.63
τ00 trait 0.12
τ11 subID.neighAveOutSE.Z 0.24
τ11 subID.novelHeld Out 0.00
ρ01 subID.neighAveOutSE.Z -0.07
ρ01 subID.novelHeld Out -1.00
N subID 200
N trait 147
Observations 29060
Marginal R2 / Conditional R2 0.033 / NA

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * novel * outDegree.Z + desirability.Z + ( neighAveInSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.710
(1.494 – 1.957)
0.118 7.806 <0.001
neighAveInSE Z 1.186
(1.091 – 1.289)
0.051 4.001 <0.001
novel [Held Out] 0.986
(0.935 – 1.040)
0.027 -0.517 0.605
outDegree Z 1.063
(0.987 – 1.144)
0.040 1.607 0.108
desirability Z 1.223
(1.140 – 1.313)
0.044 5.600 <0.001
neighAveInSE Z * novel
[Held Out]
0.974
(0.922 – 1.028)
0.027 -0.963 0.336
neighAveInSE Z *
outDegree Z
1.078
(1.039 – 1.118)
0.020 4.032 <0.001
novel [Held Out] *
outDegree Z
1.021
(0.968 – 1.077)
0.028 0.761 0.447
(neighAveInSE Z * novel
[Held Out]) * outDegree Z
0.957
(0.904 – 1.013)
0.028 -1.527 0.127
Random Effects
σ2 3.29
τ00 subID 0.67
τ00 trait 0.13
τ11 subID.neighAveInSE.Z 0.20
τ11 subID.novelHeld Out 0.00
ρ01 subID.neighAveInSE.Z 0.06
ρ01 subID.novelHeld Out -0.60
N subID 200
N trait 148
Observations 29282
Marginal R2 / Conditional R2 0.028 / NA

Does generalization depend on indegree?

Similarity-Weighted Self-Evaluations

m <- glmer( ingChoiceN ~ WSR.Z * novel * inDegree.Z + desirability.Z + ( WSR.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.663
(1.370 – 2.020)
0.165 5.138 <0.001
WSR Z 1.890
(1.448 – 2.466)
0.257 4.682 <0.001
novel [Held Out] 0.984
(0.933 – 1.038)
0.027 -0.606 0.545
inDegree Z 0.982
(0.919 – 1.049)
0.033 -0.535 0.592
desirability Z 1.249
(1.173 – 1.329)
0.040 6.938 <0.001
WSR Z * novel [Held Out] 0.963
(0.912 – 1.017)
0.027 -1.341 0.180
WSR Z * inDegree Z 1.050
(1.014 – 1.086)
0.018 2.775 0.006
novel [Held Out] *
inDegree Z
1.048
(0.995 – 1.105)
0.028 1.760 0.078
(WSR Z * novel [Held
Out]) * inDegree Z
0.957
(0.907 – 1.010)
0.026 -1.581 0.114
Random Effects
σ2 3.29
τ00 subID 1.26
τ00 trait 0.12
τ11 subID.WSR.Z 2.59
τ11 subID.novelHeld Out 0.00
ρ01 subID.WSR.Z 0.06
ρ01 subID.novelHeld Out 0.79
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.120 / NA
ggpredict(m, c("WSR.Z", "novel")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * novel * inDegree.Z + desirability.Z + ( predicted.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.641
(1.425 – 1.891)
0.119 6.858 <0.001
predicted Z 1.577
(1.311 – 1.896)
0.148 4.839 <0.001
novel [Held Out] 0.990
(0.938 – 1.044)
0.027 -0.387 0.699
inDegree Z 0.981
(0.916 – 1.051)
0.034 -0.544 0.587
desirability Z 1.261
(1.181 – 1.346)
0.042 6.954 <0.001
predicted Z * novel [Held
Out]
0.961
(0.910 – 1.014)
0.026 -1.460 0.144
predicted Z * inDegree Z 1.045
(1.011 – 1.081)
0.018 2.567 0.010
novel [Held Out] *
inDegree Z
1.042
(0.989 – 1.098)
0.028 1.552 0.121
(predicted Z * novel
[Held Out]) * inDegree Z
0.960
(0.910 – 1.012)
0.026 -1.506 0.132
Random Effects
σ2 3.29
τ00 subID 0.53
τ00 trait 0.13
τ11 subID.predicted.Z 0.64
τ11 subID.novelHeld Out 0.00
ρ01 subID.predicted.Z 0.26
ρ01 subID.novelHeld Out -0.40
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.072 / NA

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * novel * inDegree.Z + desirability.Z + ( er.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.590
(1.330 – 1.901)
0.145 5.085 <0.001
er Z 1.418
(1.124 – 1.788)
0.168 2.949 0.003
novel [Held Out] 0.980
(0.929 – 1.033)
0.027 -0.761 0.447
inDegree Z 0.988
(0.923 – 1.058)
0.034 -0.338 0.735
desirability Z 1.263
(1.184 – 1.347)
0.042 7.071 <0.001
er Z * novel [Held Out] 0.961
(0.910 – 1.014)
0.026 -1.469 0.142
er Z * inDegree Z 1.049
(1.014 – 1.086)
0.018 2.763 0.006
novel [Held Out] *
inDegree Z
1.046
(0.992 – 1.102)
0.028 1.673 0.094
(er Z * novel [Held Out])
* inDegree Z
0.961
(0.910 – 1.014)
0.026 -1.463 0.144
Random Effects
σ2 3.29
τ00 subID 1.02
τ00 trait 0.13
τ11 subID.er.Z 1.95
τ11 subID.novelHeld Out 0.00
ρ01 subID.er.Z -0.01
ρ01 subID.novelHeld Out -0.40
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.050 / NA

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * novel * inDegree.Z + desirability.Z + ( slope.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.655
(1.382 – 1.983)
0.152 5.479 <0.001
slope Z 1.535
(1.201 – 1.962)
0.192 3.426 0.001
novel [Held Out] 0.981
(0.930 – 1.034)
0.027 -0.713 0.476
inDegree Z 0.988
(0.924 – 1.058)
0.034 -0.335 0.738
desirability Z 1.260
(1.182 – 1.345)
0.042 7.024 <0.001
slope Z * novel [Held
Out]
0.959
(0.908 – 1.012)
0.026 -1.534 0.125
slope Z * inDegree Z 1.050
(1.015 – 1.087)
0.018 2.817 0.005
novel [Held Out] *
inDegree Z
1.045
(0.991 – 1.101)
0.028 1.623 0.104
(slope Z * novel [Held
Out]) * inDegree Z
0.961
(0.910 – 1.014)
0.026 -1.464 0.143
Random Effects
σ2 3.29
τ00 subID 1.01
τ00 trait 0.13
τ11 subID.slope.Z 2.13
τ11 subID.novelHeld Out 0.00
ρ01 subID.slope.Z -0.05
ρ01 subID.novelHeld Out -0.50
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.066 / NA

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * novel * inDegree.Z + desirability.Z + ( neighAveAllSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.701
(1.488 – 1.945)
0.116 7.777 <0.001
neighAveAllSE Z 1.262
(1.137 – 1.402)
0.068 4.355 <0.001
novel [Held Out] 0.989
(0.938 – 1.044)
0.027 -0.392 0.695
inDegree Z 0.990
(0.925 – 1.059)
0.034 -0.300 0.764
desirability Z 1.262
(1.184 – 1.345)
0.041 7.120 <0.001
neighAveAllSE Z * novel
[Held Out]
0.976
(0.924 – 1.030)
0.027 -0.886 0.376
neighAveAllSE Z *
inDegree Z
1.063
(1.026 – 1.101)
0.019 3.379 0.001
novel [Held Out] *
inDegree Z
1.043
(0.990 – 1.099)
0.028 1.577 0.115
(neighAveAllSE Z * novel
[Held Out]) * inDegree Z
0.949
(0.898 – 1.003)
0.027 -1.857 0.063
Random Effects
σ2 3.29
τ00 subID 0.63
τ00 trait 0.13
τ11 subID.neighAveAllSE.Z 0.36
τ11 subID.novelHeld Out 0.00
ρ01 subID.neighAveAllSE.Z -0.07
ρ01 subID.novelHeld Out -0.52
N subID 200
N trait 148
Observations 29329
Marginal R2 / Conditional R2 0.033 / NA

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * novel * inDegree.Z + desirability.Z + ( neighAveOutSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.722
(1.508 – 1.966)
0.116 8.035 <0.001
neighAveOutSE Z 1.215
(1.114 – 1.326)
0.054 4.374 <0.001
novel [Held Out] 0.983
(0.932 – 1.037)
0.027 -0.628 0.530
inDegree Z 0.986
(0.922 – 1.055)
0.034 -0.402 0.688
desirability Z 1.263
(1.185 – 1.346)
0.041 7.174 <0.001
neighAveOutSE Z * novel
[Held Out]
0.973
(0.922 – 1.028)
0.027 -0.973 0.330
neighAveOutSE Z *
inDegree Z
1.053
(1.017 – 1.091)
0.019 2.887 0.004
novel [Held Out] *
inDegree Z
1.049
(0.995 – 1.105)
0.028 1.760 0.078
(neighAveOutSE Z * novel
[Held Out]) * inDegree Z
0.946
(0.896 – 1.000)
0.026 -1.971 0.049
Random Effects
σ2 3.29
τ00 subID 0.64
τ00 trait 0.13
τ11 subID.neighAveOutSE.Z 0.24
τ11 subID.novelHeld Out 0.00
ρ01 subID.neighAveOutSE.Z -0.07
ρ01 subID.novelHeld Out -1.00
N subID 200
N trait 147
Observations 29060
Marginal R2 / Conditional R2 0.029 / NA

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * novel * inDegree.Z + desirability.Z + ( neighAveInSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
## boundary (singular) fit: see help('isSingular')
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.710
(1.494 – 1.957)
0.118 7.800 <0.001
neighAveInSE Z 1.200
(1.103 – 1.306)
0.052 4.222 <0.001
novel [Held Out] 0.985
(0.934 – 1.039)
0.027 -0.563 0.574
inDegree Z 0.994
(0.928 – 1.064)
0.035 -0.187 0.852
desirability Z 1.259
(1.181 – 1.344)
0.042 6.985 <0.001
neighAveInSE Z * novel
[Held Out]
0.972
(0.920 – 1.027)
0.027 -1.011 0.312
neighAveInSE Z * inDegree
Z
1.063
(1.025 – 1.102)
0.019 3.324 0.001
novel [Held Out] *
inDegree Z
1.046
(0.992 – 1.102)
0.028 1.673 0.094
(neighAveInSE Z * novel
[Held Out]) * inDegree Z
0.968
(0.916 – 1.023)
0.027 -1.153 0.249
Random Effects
σ2 3.29
τ00 subID 0.66
τ00 trait 0.13
τ11 subID.neighAveInSE.Z 0.20
τ11 subID.novelHeld Out 0.00
ρ01 subID.neighAveInSE.Z 0.05
ρ01 subID.novelHeld Out -0.56
N subID 200
N trait 148
Observations 29282
Marginal R2 / Conditional R2 0.027 / NA

Backwards solution: Can you predict self-evaluations from similarity to ingroup and outgroup choices?

Main effect

m <- lmer( scale(selfResp) ~ scale(inGsim) + scale(outGsim) + (  scale(inGsim) + scale(outGsim) | subID) + (1 | trait), data = fullTrain)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0027381 (tol = 0.002, component 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "B", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects B SE t p
(Intercept) -0.020
(-0.096 – 0.057)
0.039 -0.505 0.614
inGsim 0.163
(0.113 – 0.214)
0.026 6.352 <0.001
outGsim -0.024
(-0.069 – 0.021)
0.023 -1.043 0.297
Random Effects
σ2 0.68
τ00 subID 0.17
τ00 trait 0.09
τ11 subID.scale(inGsim) 0.05
τ11 subID.scale(outGsim) 0.05
ρ01 subID.scale(inGsim) 0.05
ρ01 subID.scale(outGsim) -0.03
ICC 0.31
N subID 197
N trait 148
Observations 17362
Marginal R2 / Conditional R2 0.022 / 0.323

Moderated by condition

m <- lmer( scale(selfResp) ~ scale(inGsim)*outgroup + scale(outGsim)*outgroup + (  scale(inGsim) + scale(outGsim) | subID) + ( outgroup | trait), data = fullTrain)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+01
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "B", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects B SE t p
(Intercept) 0.001
(-0.113 – 0.115)
0.058 0.021 0.983
inGsim 0.174
(0.095 – 0.252)
0.040 4.312 <0.001
outgroup [Not UCR] 0.024
(-0.122 – 0.170)
0.074 0.317 0.751
outgroup [UCLA] -0.086
(-0.231 – 0.060)
0.074 -1.155 0.248
outGsim -0.007
(-0.080 – 0.066)
0.037 -0.186 0.852
inGsim * outgroup [Not
UCR]
0.031
(-0.070 – 0.131)
0.051 0.602 0.547
inGsim * outgroup [UCLA] -0.042
(-0.139 – 0.055)
0.049 -0.854 0.393
outgroup [Not UCR] *
outGsim
-0.076
(-0.176 – 0.024)
0.051 -1.485 0.138
outgroup [UCLA] * outGsim 0.021
(-0.076 – 0.118)
0.050 0.420 0.675
Random Effects
σ2 0.68
τ00 subID 0.17
τ00 trait 0.09
τ11 subID.scale(inGsim) 0.05
τ11 subID.scale(outGsim) 0.05
τ11 trait.outgroupNot UCR 0.00
τ11 trait.outgroupUCLA 0.00
ρ01 0.03
-0.01
-1.00
-0.77
N subID 197
N trait 148
Observations 17362
Marginal R2 / Conditional R2 0.036 / NA
ggpredict(m, c("inGsim","outgroup")) %>% plot(show.title=F) + xlab("Similarity to Ingroup Choices") + ylab("Self-Evaluation") + jtools::theme_apa()

Moderated by Group Homophily

People who mentally segregrate the groups also self-evaluate more similar to their later group choices.

# m <- lmer( scale(selfResp) ~ scale(inGsim) * scale(groupHomoph) + scale(outGsim) + (  scale(inGsim) + scale(outGsim) | subID) + (1 | trait), data = fullTrain)
# summary(m)
# tidy(m,conf.int=TRUE,effects="fixed")
# ggpredict(m, c("inGsim","groupHomoph")) %>% plot(show.title=F) + xlab("Similarity to Ingroup Choices") + ylab("Self-Evaluation") + jtools::theme_apa()

Individual differences moderation of self-anchoring

More self-projection for those who perceive group as more entitative?

m <- glmer( ingChoiceN ~ WSR.Z * Ent.Z + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.627
(1.335 – 1.983)
0.164 4.824 <0.001
WSR Z 1.857
(1.418 – 2.432)
0.255 4.501 <0.001
Ent Z 1.133
(0.934 – 1.373)
0.111 1.269 0.204
WSR Z * Ent Z 1.117
(0.862 – 1.448)
0.148 0.839 0.402
Random Effects
σ2 3.29
τ00 subID 1.28
τ00 trait 0.17
τ11 subID.WSR.Z 2.60
ρ01 subID 0.07
ICC 0.55
N subID 199
N trait 148
Observations 29184
Marginal R2 / Conditional R2 0.058 / 0.577

More self-projection for those who have been at UCR for shorter time?

m <- glmer( ingChoiceN ~ WSR.Z * Years + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 2.180
(1.315 – 3.614)
0.562 3.023 0.003
WSR Z 1.726
(0.891 – 3.344)
0.582 1.618 0.106
Years 0.881
(0.717 – 1.082)
0.092 -1.212 0.225
WSR Z * Years 1.037
(0.797 – 1.349)
0.139 0.271 0.786
Random Effects
σ2 3.29
τ00 subID 1.28
τ00 trait 0.17
τ11 subID.WSR.Z 2.58
ρ01 subID 0.07
ICC 0.55
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.051 / 0.573

More self-projection for higher self-esteem?

Higher self-esteem people self-project more

m <- glmer( ingChoiceN ~ WSR.Z * SE + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 0.787
(0.556 – 1.114)
0.140 -1.349 0.177
WSR Z 1.404
(1.054 – 1.871)
0.206 2.317 0.020
SE 19.366
(6.316 – 59.377)
11.070 5.184 <0.001
WSR Z * SE 2.529
(1.602 – 3.993)
0.589 3.983 <0.001
Random Effects
σ2 3.29
τ00 subID 1.26
τ00 trait 0.15
τ11 subID.WSR.Z 2.57
ρ01 subID 0.07
ICC 0.55
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.049 / 0.570
ggpredict(m, c("SE","WSR.Z")) %>% plot(show.title=F) + xlab("Self-Esteem") + ylab("Self-Evaluation") + jtools::theme_apa()
## Data were 'prettified'. Consider using `terms="SE [all]"` to get smooth plots.

Do people higher in social identification self-project more?

No evidence

m <- glmer( ingChoiceN ~ WSR.Z * MGIS + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 0.676
(0.284 – 1.611)
0.299 -0.883 0.377
WSR Z 1.177
(0.393 – 3.527)
0.659 0.291 0.771
MGIS 1.208
(1.006 – 1.451)
0.113 2.026 0.043
WSR Z * MGIS 1.097
(0.869 – 1.384)
0.130 0.776 0.438
Random Effects
σ2 3.29
τ00 subID 1.20
τ00 trait 0.17
τ11 subID.WSR.Z 2.52
ρ01 subID 0.05
ICC 0.54
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.064 / 0.571

Do people higher in need for cognition self-project more?

m <- glmer( ingChoiceN ~ WSR.Z * NFC + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 3.682
(1.335 – 10.157)
1.906 2.518 0.012
WSR Z 1.527
(0.395 – 5.910)
1.054 0.613 0.540
NFC 0.809
(0.624 – 1.048)
0.107 -1.604 0.109
WSR Z * NFC 1.060
(0.752 – 1.493)
0.185 0.331 0.741
Random Effects
σ2 3.29
τ00 subID 1.28
τ00 trait 0.17
τ11 subID.WSR.Z 2.61
ρ01 subID 0.07
ICC 0.55
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.048 / 0.573

Do people higher in need to belong project more?

m <- glmer( ingChoiceN ~ WSR.Z * NTB + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 1.923
(0.698 – 5.303)
0.995 1.264 0.206
WSR Z 2.010
(0.519 – 7.787)
1.389 1.010 0.312
NTB 0.952
(0.704 – 1.287)
0.147 -0.320 0.749
WSR Z * NTB 0.978
(0.651 – 1.468)
0.203 -0.109 0.914
Random Effects
σ2 3.29
τ00 subID 1.29
τ00 trait 0.17
τ11 subID.WSR.Z 2.59
ρ01 subID 0.06
ICC 0.55
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.051 / 0.574

Do people higher in self-prototypicality self-project more?

m <- glmer( ingChoiceN ~ WSR.Z * Proto + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
  Ingroup Prediction
Fixed Effects OR SE z p
(Intercept) 0.851
(0.434 – 1.670)
0.293 -0.469 0.639
WSR Z 1.094
(0.445 – 2.691)
0.502 0.195 0.845
Proto 1.127
(0.999 – 1.272)
0.069 1.949 0.051
WSR Z * Proto 1.099
(0.935 – 1.291)
0.091 1.141 0.254
Random Effects
σ2 3.29
τ00 subID 1.21
τ00 trait 0.17
τ11 subID.WSR.Z 2.54
ρ01 subID 0.04
ICC 0.54
N subID 200
N trait 148
Observations 29331
Marginal R2 / Conditional R2 0.062 / 0.572